In the present analysis we will study the interest of the AI community on a AI benchmarks. We focus on “interest” rather than “progress” for AI benchmarks as this is something we can compute using some proxies. In this particular case, we use the number of (normalised) hits (number of documents) obtained from AItopics per benchmark over the last decade (2008-2019). Note that the results from 2019 are incomplete.

The benchmarks in the present analysis rely on our own previous analysis and annotation of papers, as well as on open resources such as Papers With Code, including data from from several repositories (e.g, EFF, NLP-progress, SQuAD, RedditSota, etc.).

Mean Interest per AI benchmak

In this two plots we show how the mean interest per AI benchmark has varied among different periods.

Mapping between AI benchmarks and Cognitive Abilities

Graphical representation

  • Benchmarks are grouped and coloured by area (groups from https://paperswithcode.com/)
  • Cognitive abilities are coloured in black and its size represent its relevance (total sum in the mapping) ponderated by interest (previous plot).
  • Edges represent that an ability is assigned to a task.
  • The width of the edges represent “interest” on the benchmark: the wider the edge, the more interest from the community during the last decade (mean).

Note that we can perform exactly the same analysis focusing on different (ranges of) years and obtaining the same graph but the width of the edges may vary (a little bit).

(Network are interactive!)

Period 2008-2010

plotVis(prepareVis(dataA)[[1]], categories = data$category)

Period 2011-2013

plotVis(prepareVis(dataB)[[1]], categories = data$category)

Period 2014-2016

plotVis(prepareVis(dataC)[[1]], categories = data$category)

Period 2017-2019

plotVis(prepareVis(dataD)[[1]], categories = data$category)

Relevance of the cognitive abilities in diferent periods

Total sum (binary mapping between cognitive abilities and AI benchmarks)

cogAbs = c("MP", "SI", "VP", "AP", "AS", "PA", "CE", "CO", "EC", "NV", "CL", "QL", "MS", "MC")
barplot(colSums(select(data, cogAbs)))

Total sum (binary mapping) pondered by interest in different periods

periods <- list(2008:2010, 2011:2013, 2014:2016, 2017:2019)

all <- plotProgressPeriods(data, periods)
## [1] 2008 2009 2010
## Warning in if (interestYears == "All") {: the condition has length > 1 and
## only the first element will be used
## [1] 2011 2012 2013
## Warning in if (interestYears == "All") {: the condition has length > 1 and
## only the first element will be used
## [1] 2014 2015 2016
## Warning in if (interestYears == "All") {: the condition has length > 1 and
## only the first element will be used
## [1] 2017 2018 2019
## Warning in if (interestYears == "All") {: the condition has length > 1 and
## only the first element will be used
all.s <- summarise(group_by(all, period, variable), mean = mean(value))

ggplotly(ggplot(all.s, aes(variable,mean, fill = period)) + geom_bar(stat = "identity",position = "dodge") + xlab("") + ylab("Mean Interest") + 
           scale_fill_brewer(palette = "Paired") + theme_minimal())

Total sum (binary mapping) pondered by interest in different years

periods <- 2008:2019

all <- plotProgressPeriods(data, periods)
## [1] 2008
## [1] 2009
## [1] 2010
## [1] 2011
## [1] 2012
## [1] 2013
## [1] 2014
## [1] 2015
## [1] 2016
## [1] 2017
## [1] 2018
## [1] 2019
all.s <- summarise(group_by(all, period, variable), mean = mean(value))

ggplotly(ggplot(all.s, aes(variable,mean, fill = period)) + geom_bar(stat = "identity",position = "dodge") + xlab("") + ylab("Mean Interest") + 
           scale_fill_brewer(palette = "Paired") + theme_minimal())

Interest per benchmark.

(Groups from https://paperswithcode.com/)

Computer Vision

plotIterest.Cat(interest.m, "Computer Vision")

Audio

plotIterest.Cat(interest.m, "Audio")

Graphs

plotIterest.Cat(interest.m, "Graphs")

Natural Language Processing

plotIterest.Cat(interest.m, "Natural Language Processing")

Playing Games

plotIterest.Cat(interest.m, "Playing Games")

Miscellaneous

plotIterest.Cat(interest.m, "Miscellaneous")

Medical

plotIterest.Cat(interest.m, "Medical")

Methodology

plotIterest.Cat(interest.m, "Methodology")

Speech

plotIterest.Cat(interest.m, "Speech")

Reasoning

plotIterest.Cat(interest.m, "Reasoning")

Time Series

plotIterest.Cat(interest.m, "Time Series")

Computer Code

plotIterest.Cat(interest.m, "Computer Code")